In this paper, an oriented auto-tuning niching algorithm (OANA) is proposed for the design optimization of a permanent magnet assisted synchronous reluctance motor (PMa-SynRM) for a pedal-assist system electric bicycle (PAS-EB). The OANA is a fast and accurate optimization algorithm for finding the optimal points in multi-modal functions. Specifically, the OANA remedies the shortcomings of the conventional auto-tuning niching genetic algorithm by adopting a probability method that is based on the navigational pathways. The OANA showed excellent performance by utilizing mathematical multi-modal test functions. Then, the OANA was applied to cogging torque optimization for a PMa-SynRM design of the PAS-EB and found several optimal points. Finally, the optimum design is derived by comparing the average torque and torque ripple of the obtained design points.

本文提出了一种面向方向的自动调整小生境算法（OANA），用于踏板辅助系统电动自行车（PAS-EB）的永磁辅助同步磁阻电机（PMa-SynRM）的设计优化。OANA是一种快速准确的优化算法，用于在多模式函数中找到最佳点。具体而言，OANA通过采用基于导航路径的概率方法来弥补传统的自动调整小生境遗传算法的缺点。OANA通过使用数学多模式测试功能显示了出色的性能。然后，将OANA应用于PAS-EB的PMa-SynRM设计的齿槽转矩优化，并发现了几个最佳点。最后，